Term-weighting approaches in automatic text retrieval
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Models for retrieval with probabilistic indexing
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Natural language processing for information retrieval
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Pivoted document length normalization
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Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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Information Retrieval
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Computational Linguistics
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ANLC '94 Proceedings of the fourth conference on Applied natural language processing
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AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Expert Systems with Applications: An International Journal
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The importance of good weighting methods in information retrieval --- methods that stress the most useful features of a document or query representative --- is examined. Evidence is presented that good weighting methods are more important than the feature selection process and it is suggested that the two need to go hand-in-hand in order to be effective. The paper concludes with a method for learning a good weight for a term based upon the characteristics of that term.